WP 15

The research leading to these results has received funding from the European Research Council under the European ERC Grant Agreement n. StG-313617 (SWELL-FER: Subjective Well-being and Fertility): PI. Letizia Mencarini

It takes two to tango: couples' happiness and childbearing

Arnstein Aassvea Bruno Arpinob

Nicoletta Balboa

a) Dondena Centre for Research on Social Dynamics, Bocconi University

b) Universitat Pompeu Fabra

1

It takes two to tango: couples' happiness and childbearing

Arnstein Aassve1, Bruno Arpino2, Nicoletta Balbo1

1 - Department of Policy Analysis and Public Management and Carlo F. Dondena Centre for Research on

Social Dynamics and Public Policies, Bocconi University, Milan, Italy; arnstein.aassve@unibocconi.it;

balbo.nicoletta@unibocconi.it.

2 - Department of Political and Social Sciences and the Research and Expertise Centre for Survey

Methodology (RECSM), Pompeu Fabra University, Barcelona, Spain; bruno.arpino@upf.edu.

Abstract

Existing literature has so far considered the role of the individual's subjective well-being

on fertility, neglecting the importance of the partner’s well-being. Using data from the

British Household Panel Survey (BHPS) and event history models estimated separately

by parity, we find that in a couple, women's happiness matter more than that of the male

partner in terms of having the first child. Specifically, we observe that couples in which

either partner is happier are more at risk of having the first child but the effect is

strongest with higher happiness of the woman. For the transition to the second child we

find that couples in which the woman is either happier or less happy than usual, are

associated with a lower risk of childbirth. We moreover find support for a multiplicative

effect of partners’ SWB on the decision to have a first child. Our results show that

failing to acknowledge that the subjective well-being of both partners matter for the

inherently joint decision making of childbearing, can lead to an uncompleted view of

how subjective well-being affects fertility.

2

1. Introduction

No longer limited to psychology, studies on subjective wellbeing and the way it links

with various behavioral aspects, are finding their way into the social sciences. Within

demography, there has been a particular focus on the relationship between subjective

wellbeing and childbearing. Recent years have witnessed a large number of studies that

consider the dynamic interplay between childbearing and subjective wellbeing (SWB)

(e.g., Billari 2009; Aassve et al., 2012; Margolis and Myrskylä 2011, 2015). Although

the majority of them focuses on the effect of fertility on SWB, others investigate the

opposite relationship, that is, the role of SWB on intended and actual fertility.

Existing theories and findings are rather mixed. Higher life satisfaction has been found

to predict intended (Billari 2009; Perelli-Harris 2006) and actual fertility (Parr 2010).

More recent studies have showed that an individual’s SWB is particularly relevant for

having a second child, because such decision is very much a function of the experience

of having had the first child - and importantly - the satisfaction associated with it (Le

Moglie 2015; Margolis and Myrskylä 2015). Therefore those individuals who

experience a higher life satisfaction (or a lower drop) after the first child birth are more

likely to have another one. Conversely, Mc Donald (2002) has highlighted how happier

people may refrain from having children in order not to change a positive status-quo.

Such risk aversion have been used as a possible explanation for the rise in voluntary

childlessness (Mencarini and Tanturri 2007).

The vast majority of the studies on SWB and childbearing focuses on individuals’, or

rather, the respondents’ wellbeing (e.g., Myrskylä and Margolis 2014). This is perhaps

surprising because the venture of childbearing is necessarily a joint decision of the two

partners involved (Bauer and Kneip 2013). In this paper we ask the question to what

3

extent a couple-perspective brings insights into the relationship between subjective

wellbeing and couples’ childbearing decision making. Consequently, childbearing

events constitute the dependent variable, and the key explanatory variable refers to the

reported level of happiness of both partners. The analysis is then implemented by parity,

acknowledging that potential coherency or mismatch between the two partners may

have different impact when considering becoming a parent first time, as opposed to the

decision to have another child. Of particular interest is to understand to what extent a

potential mismatch in subjective wellbeing across partners may affect their decision

making. Similarly, interest lies in understanding to what extent there are multiplicative

effects. That is, can one detect an alleviated effect on childbearing if both partners are

closely in line when it comes to their reported subjective wellbeing?

The analysis is based on the British Household Panel Survey (BHPS), from which we

observe fertility behaviors and subjective well-being of couples over a period of 18

years (from 1991 to 2008). We implement a series of event history models to investigate

whether the level of happiness of the two partners considered together, affects fertility.

2. Background

There is now a growing body of studies considering the relationship between fertility

and subjective wellbeing. Compared to earlier studies of fertility within the field of

demography, this line of analysis represents a considerable shift. But given the

introduction of the Second Demographic Transition (SDT) some 40 years ago, the

paradigm of subjective wellbeing comes naturally when considering childbearing

behavior. Inspired by the rise of Post-Materialism (Inglehart 1971), the main idea of the

4

SDT is that the family has become less essential (Van de Kaa 1987), and so new

demographic behavior are emerging, which would include divorce, cohabitation and

out-of-wedlock childbearing and, importantly, fertility postponement and decline

(Aassve et al. 2013). Implicit in the SDT lies the idea that individuals' value orientation

are changing with an increasing emphasis on freedom of expression and, importantly,

psychological wellbeing. Individuals are in a continuous quest for improving their

subjective wellbeing (Le Moglie et al. 2015), but given the ever increasing complexity

of individuals' lives, obtaining fulfillment through children becomes necessarily only

one element out of many. In other words, the wellbeing associated with childbearing,

increasingly depends on the timing, context and the way it is compatible with a range of

other activities that individuals now give high priority.

There is consequently no surprise that in recent years a series of studies

analyzing the relationship between happiness and childbearing has emerged (Aassve et

al. 2012, 2015; Balbo and Arpino 2014; Baranowska and Matysiak 2011; Kohler et al.

2005; Margolis and Myrskylä 2011, 2015; Myrskylä and Margolis 2014). Subjective

wellbeing is usually proxied either by a measure of life satisfaction, or more frequently,

the reported level of happiness, and held up against childbearing behavior.

Most of these studies focus on how individuals associate childbearing with something

positive and aim at uncover whether having children affect SWB. In other words, in the

empirical analysis, the dependent variable typically refers to the reported level of SWB,

and childbearing events are taken as the key explanatory variables. However, one

frequently observed pattern is that SWB increases prior to childbearing, whereas after

the childbearing event there is a great deal of adaption, and often the positive

anticipation effect is subsequently neutralized (Balbo and Arpino 2014; Clark et al.

5

2008; Myrskylä and Margolis 2014). The very fact that there is a significant anticipation

effect, has prompted interest to understand to what extent an increase in SWB leads to

an increase in the probability of childbearing. Some studies have therefore investigated

how the level of happiness or life satisfaction prior to childbearing affects the

subsequent decision of having a child. Billari (2009), for instance, finds that happier

people are more likely to intend to have a(nother) child. Consistent results are found by

Perelli-Harris (2006), who finds the same positive effect of SWB on intended and actual

fertility in Russia. Parr (2010), using longitudinal data, finds a significant positive

relationship between life satisfaction and subsequent fertility in Australia. Parr elaborate

on the possible mechanisms according to which SWB would affects fertility, net of

other, relevant socio-demographic factors (e.g., employment, income). First, the

presence of a partner would contribute to an individual’s life satisfaction as well as to

fertility. Moreover, a satisfying partnership (which would positively affect an

individual’s SWB) would increase the likelihood of having a child for the two partners

through contributing to the stability and the likelihood of the union itself. Third, because

parents desire to have happy children, happier parents may increase the likelihood that

their children will become so, thereby increasing also their likelihood to have one.

Conversely, depression and stress have been found to reduce fecundity and in turn

fertility. Finally, as Kohler et al. (2005) stated, the satisfaction deriving from having

children contributes to the overall SWB of the parents, therefore, the parental

experience and relative satisfaction may affect further parents’ fertility. Based on this

argument, Margolis and Myrskylä (2015), investigates how the change in life

satisfaction of parents after the birth of the first child influences the subsequent decision

6

to have another one. They find that those parents who experience a lower drop in life

satisfaction after first childbirth are those more likely to progress to parity 2.

Another relevant study is the one of Le Moglie et al. (2015). They use the German

Socio Economic Panel Survey to analyze how SWB affects the likelihood of having

children. Their study puts a particular focus on the way personality traits interacts with

both SWB and childbearing, and importantly, they do so separately by parity. They find

that higher SWB leads to a higher likelihood of childbearing, but only for the second

child. For the onset of parenthood, and having the third child, there is no effect.

However, their results are of high importance, because low fertility, other than driven by

childlessness, is in large part explained by lower progression to having the second child.

Moreover, for the progression to the second child, the effect is significant only for

women.

What has so far been neglected in the contributions on the nexus between SWB and

childbearing is that childbearing is necessarily a joint decision between two partners,

which implies that the SWB of both partners should be jointly considered as

determinant of fertility. Almost all of the existing studies take the respondent as the

unit of analysis - holding his or her SWB together with childbearing events. In other

words, if one believes that SWB of the respondent has a direct effect on childbearing

behavior, which is indeed demonstrated by Le Moglie et al. (2015), then intuitively one

would also expect the SWB of the partner to play a role. Exactly how the SWB of the

respondent interacts with the SWB of the partner for childbearing decision making is

not at all clear - nor is it obvious how any such interactions may differ across parity.

7

There is however strong evidence that the reported SWB of the respondent is not

independent of the SWB of the partner. In a recent study, Powdthavee (2009), using the

British Household Panel Survey (BHPS), demonstrates a positive correlation between

spouses' self reported life satisfaction. He also explores the underlying mechanism

behind the correlation, and postulates three key factors. First, individuals with an innate

inclination towards higher life satisfaction may also partner individuals who are similar

in this respect. This follows up on an established literature on assortative mating

(Becker et al. 1977; Greenwood et al. 2014). Second, partnerships allow sharing of

physical and emotional resources that are unavailable if remaining single, and third, any

observed correlation may be a result of direct spillover in SWB within the couple. This

last mechanism refers to the idea that if one partner cares about the other, then the SWB

of the latter becomes a significant driver of the SWB of the former - and vice versa.

Using a dynamic panel model and adjusting for measurement errors, Powdthavee

indeed finds evidence of significant spillover effects.

Of interest in our context however, is to what extent partners' wellbeing may

affect objective measures of demographic behavior, and specifically the decision of

having a child. There is a large literature demonstrating that dissimilarity in partner's

characteristics tend to affect marital stability, the argument being that dissimilarity

associates positively towards marital disruption (Jalovaara 2003; Clarkwest 2007;

Milewski and Kulu 2014). When it comes to assessing the effect of dissimilarity

measured in terms of SWB, the literature is less developed, but there are exceptions.

Guven et al. (2012) use longitudinal data from Germany, UK and Australia, to assess to

what extent a gap in reported SWB of the spouses affects the likelihood of divorce.

Using fixed effect estimation techniques, they find that indeed a higher satisfaction gap

8

gives a higher likelihood of partnership dissolution - and these results are robust to a

range of specifications - and across the three longitudinal surveys considered. Their

argument is that when it comes to assessing the role of one's own SWB with respect to

objective measureable outcomes, such as a divorce, the key reference group is in fact

that of the spouse. The analysis of Guven et al. (2012) also uncover other interesting

insights. For instance, the positive effect of the SWB gap on divorce, is not only driven

by any deviation away from the baseline case at the time of partnering, but also that the

absolute level of the gap matters. Secondly, the effects are potentially asymmetric, and

for divorce, they find that its likelihood increases especially when the wife has a lower

level of SWB than the husband, but not the other way around.

To the best of our knowledge there are so far no studies considering the effect of

partners' joint SWB on childbearing. For our analysis, the study by Le Moglie et al.

(2015) gives important clues to what expect. First, one may expect different effects

depending on parity. Secondly, there might be gender dominance when deciding to have

children. In line with Testa et al. (2011), we are interested in understanding if the SWB

of the female partner has a stronger impact on the childbearing decision than the male

partner. The research questions of this study are consequently summarized as follows:

1) Does the subjective well-being of one partner, either the female or male, prevail over

the other in the decision of having a(another) child? 2) Is there a multiplicative effect of

the subjective well-being of the two partners in the decision of having a(nother) child?

Building on the findings of Testa et al. (2011), that shows that women have a greater

influence on fertility decisions than men, we postulate that SWB of the female partner

plays a stronger role than male partner’s SWB, though a priori we do not have a

specific hypothesis regarding differential effects for parities. We moreover pose another

9

hypothesis about the multiplicative effect of both partners’ happiness. Specifically we

test whether having a child is more likely in couples where both partners have a high

level of SWB compared with couples in which only one partner has a high SWB.

3. Data and methods

3.1. Sample selection and measurements

We use the British Household Panel Survey (BHPS), an annual panel survey consisting

of a nationally representative sample of about 5,500 households recruited in 1991,

containing a total of approximately 10,000 interviewed individuals. Participants are re-

interviewed each successive year for 18 years and, if they split from original households

to form new households, they are followed and all adult members of these households

are also interviewed. Similarly, new members joining sample households become

eligible for interview and children are interviewed as they reach the age of 16. The

BHPS dataset is well-suited to investigating the relationship between happiness and

fertility because it provides information on several socio-economic characteristics,

fertility history, and subjective well-being measured over time.

We select only observations for couples, either married or cohabiting, which

means that we exclude from the analyses observation-years where individuals where

observed as single, divorced or widowed and we also excluded partnered individuals for

which the information on the partner was missing. We select all couple-year

observations for heterosexual couples where the woman is aged 16-45 and the man is

aged 16-50. To allow the effect of happiness to differ by parity we considered

separately the transition to the first, second and higher order births. After deletion of

10

missing values and a few cases of multiple births our working samples consists of 645,

554 and 264 couples for the analyses of the first, second and higher order births

respectively, corresponding to 2101, 1706 and 982 couple-year observations. Of course

the same couple could experience more than one transition during the observation

period, whereas others may have entered the survey with already one or more children.

The key explanatory variables measure women and men’s happiness. The BHPS

questionnaire ask: Have you recently been feeling reasonably happy, all things

considered?". Possible responses are: more than usual, same as usual, less so and much

less. This question is asked in each wave of the survey and therefore it was preferred to

the question on life satisfaction that is missing in 6 waves of BHPS (wave 1 to 5 and

wave 11). As reported by Myrskylä and Margolis (2014), happiness and life satisfaction

are highly correlated and offer consistent results. Since the percentage of respondents

who declared to feel "much less happy than usual" was extremely low (< 3% for both

men and women), we decided to group this and the "less so" categories. We introduced

two categorical variables in our regression models measuring women and men reported

happiness separately: women (men) "happier than usual" and "less happy than usual".

The reference category is "as happy as usual".

To test for possible interaction between partners' happiness, in a second analysis

we built 9 couple types based on the combination of the happiness levels of both

partners: both man and woman less happy than usual; man less happy than usual and

women at the usual level and so on (reference: both partners at the usual level).

Keeping in line with the existing literature (Margolis and Myrskylä, 2015;

Myrskylä and Margolis and 2014; Pollmann-Shult 2014), we introduced a set of control

variables. We introduced a dummy variable indicating whether the couple is cohabiting

11

or married (Keizer and Schenk 2012). All the other control variables refer to

individuals' characteristics and are measured for both partners separately. In particular,

we controlled for both partners’ age, health, education and working status. Age and

health are introduced as numerical variables. Health is measured by self-rated health on

a five-point scale (higher scores indicate worse health). Working status is introduced

trough a set of two dummy variables (inactive, unemployed), “employed” being the

reference category. Education is measured by a categorical variable indicating the

highest level of education attained by the individual, that is, degree (reference category),

diploma, vocational school, lower school level.

3.2. Empirical approach

We analyze the transition to the first (and higher order births) using discrete-time event

history logit models (see e.g., Allison, 1982). Formally, it is assumed that time takes on

positive integer values (t = 1, 2, 3, …), we observe n independent couples (i = 1, …, n)

and the observation continues until time ti, at which point either a childbirth event

occurs or the observation is censored. We model the (discrete-time) hazard rate of

experiencing a childbearing event within the time interval t, pit, as follows:

( ) ( ) ( ) ( )( ) ,happierwomen

happy lesswomen happiermen happy lessmen plogit

4

3210it

iitit

itititt

X ηγβ

ββββα

+++

++++= (1)

12

where αt represents the logit of the baseline hazard function, that we specify as a

quadratic function of time, and β1 to β4 are the effects of our interest. Again, it is worth

noting that the reference category consists of couples who report the same level of

happiness. X is the set of control variables listed above (including both time-variant and

time-invariant covariates) and ηi represent the couple-level unobserved heterogeneity

modeled as a normal variable with zero mean and variance to be estimated.

We estimate model (1) by parity. Specifically, we consider three analyses: for

the transition to the first, second and higher order parities. For example, in the first case

couples enter the risk-set the first wave they are surveyed if childless and they are

followed till they have the first child or exit the survey. For the transition to parities

higher than the second, in principle we can observe repeated events and each time a

couple experiences a childbirth event it re-enters the set at risk of another event from the

following time point.

To test the multiplicative effect of partners' happiness we also estimated

discrete-time event history logit models including the couples’ happiness types

described above instead of the four explanatory variables showed in equation (1).

4. Results

4.1. Descriptive statistics

Table 1 presents descriptive statistics on the independent variables. In particular, for

each of the three samples used in the multivariate analyses (that correspond to the three

samples described above) we calculate the percentage in each category of the

13

independent variables in the year preceding the birth of a child. For the continuous

variables we present the average values. We observe that both the majority of men and

women declare to be as happy as usual. Both the percentage of women that are less

happy and happier than usual tend to be higher than the corresponding percentages for

men.

[INSERT TABLE 1 ABOUT HERE]

As for the control variable, we observe that the percentage of cohabiting couples

reduces from 37.4% in the first sample (couples at risk of the first child) to 21.4 in the

third sample. Both men and women are, on average, in good health (around 2 points).

While the vast majority of men are employed in all three samples, not surprisingly the

percentage of employed women decreases considerably from 74.3% in the first sample

to 44.8% in the third sample.

Table 2 provides us with an overview of how many couples have partners with

similar or different happiness level and shows which couple’s types are more common.

In this case, for the sake of brevity, we averaged over all observations and parities. The

most common type (46.2% of all the couples) is the one in which both partners are at

the usual level of happiness, followed by those couples in which either the man or the

woman is happier than usual while the partner is as happy as usual (11% and 14.6%,

respectively). There are also quite a few couples where one of the partners is less happy

than usual and the other partner's happiness is at the usual level. Other couple types are

less prevalent. Given the presence of "discordant" couples, the association between

14

partners' happiness is high but not too strong to make us worried about multi-

collinearity (the Kendall's tau rank correlation coefficient is 0.14). However, estimating

the association between some of the 9 partners' happiness combination and fertility may

be hampered by low N in some of the cells of table 2.

[INSERT TABLE 2 ABOUT HERE]

4.2. Discrete-time event history logit models results

Table 3 reports estimates of discrete-time event history logit models predicting

childbirth transitions as function of both partners’ happiness, our explanatory variables

of interest, and a set of control variables.

[INSERT TABLE 3 ABOUT HERE]

Estimates of the first model in Table 3 show that either when the man or the

woman is happier than usual, the probability of having a first child increases (as with

respect to when he or she is at the usual happiness level) and the effect is strongly

significant. Moreover, the effect is greater for women than for men. When considering

transitions to second or higher order parities, we do not find any significant effect of

men’s happiness while for women we find that both a lower and a higher happiness

level than the usual one is associated with a smaller probability of transition to the

15

second child. For transitions to higher order parities we do not find any statistically

significant effect of happiness.

Table 4 present estimates of discrete-time event history logit models where we

test for interactions between partners' happiness. We do so by introducing dummy

variables for couples types based on the combination of partners' levels of happiness

(the reference being both partners at the usual level of happiness).

Estimates in the first column show that three types of couples have significantly

higher probabilities to have a first child than couples where both partners are as happy

as usual. This includes couples where one of the partners reports an above-average level

of happiness, while the other is at the usual level. However, we notice that when the

woman is happier, the positive effect on the probability to have the first child is bigger

than the case where the man is happier than usual. We also find support for a

multiplicative effect of partners’ happiness: when both partners are happier than usual

the positive effect on fertility is stronger than when only one of the two is happier, while

the other partner's happiness is at the usual level.

For transitions to the second child, we notice that the two “extreme” types of

couples, i.e. those where both partners report a level of happiness below or above the

usual level, show significant lower probabilities to have a second child with respect to

the reference couple (where both partners are at the usual level of happiness).

Interestingly, the estimated coefficients are very similar (and not significantly different).

Also couples where the woman is happier than usual while the man is as happy as usual

are less likely to progress to the second parity.

16

For higher order transitions, consistently with the results in Table 3, we do not

significant differences among the different couple’s types.

[INSERT TABLE 4 ABOUT HERE]

5. Conclusions

The aim of the paper is to bring further insights into the relationship between subjective

wellbeing and childbearing decision making by taking a couple perspective. Existing

literature on the relationship between SWB and fertility has taken the respondent as the

unit of analysis, neglecting that the decision to have a child is a couple decision. We

uncover whether there is a gender dominance when deciding to have children. Moreover

we explore to what extent a potential coherency or mismatch in subjective wellbeing

across partners may affect their fertility decision making. By doing so we investigated

to what extent there are multiplicative effects of the two partners’ subjective wellbeing,

that is a stronger effect on childbearing if both partners report consistent happiness

level. We also implement our analysis by parity, since the subjective wellbeing of the

two partners as well as their mismatch or coherency may differently affect the decision

to have a first child or another one.

We find that a higher level of happiness of both men and women is associated

with a higher risk of having a first child, meaning that happier people are more likely to

become parents sooner. On the other hand, women's happiness seems to matter more

17

strongly than that of the male partner in the decision to have a second child. Specifically

we observe that both high and low levels of women’s happiness are associated with a

lower risk of having a second child. This latter finding implies that women may not

want to have another child not to change the positive status-quo in which they live,

suggesting an aversion to lifestyle changes (Mencarini and Tanturri, 2007). All of these

findings are relevant in showing that there is not a one sided and general gender

dominance in the fertility decision making when the SWB is concerned - instead it

depends on the parity.

Another key finding is that there is a multiplicative effect of the SWB of both

partners on the decision to have a child. We indeed observe that when both the woman

and the man report a particularly high level of happiness the probability of becoming

parents for the first time increases more than when only one of the two partners is

happier than usual. Another multiplicative effect is found after couples had the first

child: if both parents are happier than usual, this time the risk of having a second child

is the lowest. Put another way, a coherent and high level of SWB of both partners leads

to the highest (or lowest) probability for the couple to have a(nother) child, depending

on the parity This is an important result because it shows that an individual perspective

may provide only a partial understanding of the SWB-fertility relationship.

Although we acknowledge that our analysis cannot uncover strict causal effects

of parents’ SWB on fertility, we believe this paper expands existing literature by

adopting a novel couple approach in studying how happiness levels of both partner prior

to childbearing are associated with future fertility outcomes.

18

We encourage further research to investigate further the way couple dynamics

could affect partners’ wellbeing and in turn fertility choices.

19

References Aassve, A, Mazzuco, S., & Mencarin,i L. (2005). Childbearing and wellbeing: A

Comparative Analysis of Welfare Regimes in Europe. Journal of European

Social Policy, 15(3), 283- 299.

Aassve, A., Goisis, A., & Sironi, M. (2012). Happiness and Childbearing across Europe.

Social Indicator Research, 108(1), 65-86.

Aassve, A., Sironi, M., & Bassi, V. (2013). Explaining Attitudes Towards Demographic

Behaviour. European Sociological Review, 29(2), 316-333.

Aassve, A., Fuochi, G., Mencarini, L., & Mendola D. (2015). What is your couple

type? Gender ideology, housework sharing, and babies. Demographic Research,

32, 835-858.

Allison, P. D. (1982) Discrete-time methods for the analysis of event histories.

Sociological methodology, 13(1), 61-98.

Balbo, N., & Arpino, B. (2014). The role of family orientations in shaping the effect of

fertility on subjective well-being. Swellfer WP n.4, Collegio Carlo Alberto, Italy.

Baranowska, A., & Matysiak, A. (2011) Does parenthood increase happiness? Evidence

for Poland. Vienna Yearbook of Population Research, Vol. 9, (2011), pp. 307-

325.

Becker, G. S., Landes, E. M., & Michael, R. T. (1977). An Economic Analysis of

Marital Instability. Journal of Political Economy, 85, 1141- 87.

20

Bauer, G., & Kneip, T. (2013). Fertility from a couple perspective: A test of competing

decision rules on proceptive behaviour. European Sociological Review, 29(3),

535-548.

Billari, F. C. (2009). The Happiness Commonality: Fertility Decision in Low-Fertility

Settings. In UNECE (Ed.), How Generations and Gender Shape Demographic

Change (pp. 7-38). New York and Geneva: United Nations.

Clark, A.E., Diener, E., Georgellis, Y., Lucas, R.E. (2008). Lags and leads in life

satisfaction: a test of the baseline hypothesis. Economic Journal, 118 (529),

222–243.

Clarkwest A. (2007). Spousal dissimilarity, race, and marital dissolution. Journal of

Marriage and Family, 69, 639–653

Greenwood, J., Guner N., Kocharkov, G., & Santos C. (2014). Marry Your Like:

Assortative Mating and Income Inequality. American Economic Review, 104(5),

348–53.

Guven, C., Senik, C., & Stichnoth, H. (2012). You can't be happier than your wife:

Happiness gaps and divorce. Journal of Economic Behavior & Organization,

82(1), 110–130.

Inglehart, R. (1971). The Silent Revolution in Europe: Intergenerational Change in

Post-Industrial Societies, American Political Science Review, 65(4), 991–1017.

Jalovaara, M. (2003). The joint effects of marriage partners' socioeconomic positions on

the risk of divorce. Demography, 40(1): 67-81.

21

Keizer, R., & Schenk, N. (2012). Becoming a parent and relationship satisfaction: A

longitudinal dyadic perspective. Journal of Marriage and Family, 74(4), 759-

773.

Kohler, H., Behrman, J. R., & Skytthe, A. (2005). Partner + children = happiness? The

effects of partnerships and fertility on well-being. Population and Development

Review, 31(3), 407– 445.

Kravdal, O. (2014). The Estimation of Fertility Effects on Happiness: Even More

Difficult than Usually Acknowledged. European Journal of Population, doi

10.1007/s10680-013-9310-9.

Le Moglie, M., Mencarini, L., & Rapallini, C. (2015). Is it just a matter of personality?

On the role of life satisfaction in childbearing behavior. Journal of Economic

Behavior and Organization, forthcoming.

Margolis, R., & Myrskylä M. (2011). A Global Perspective on Happiness and Fertility.

Population and Development Review, 37(1), 29-56.

Margolis, R., & Myrskylä, M. (2015). Parental well-being surrounding first birth as a

determinant of further parity progression. Demography, 52(4), 1147-1166.

McDonald, P. (2002). Sustaining fertility through public policy: The range of options.

Population-E 57(3): 417-446.

Mencarini, L., & Tanturri, M.L. (2007). High fertility or childlessness: micro-level

determinants of reproductive behaviour in Italy. Population, 61(4), 389–415

22

Milewski, N and Kulu, H. (2014). Mixed marriages in Germany: a high risk of divorce

for immigrant-native couples. European Journal of Population, 30 (1), 89-113.

Myrskylä, M., & Margolis, R. (2014). Happiness: before and after the kids.

Demography, 51(5), 1843-1866.

Parr, N. (2010). Satisfaction with life as an antecedent of fertility. Demographic

Research, 22(21), 635-662.

Perelli-Harris, B. (2006). The influence of informal work and subjective well-being on

childbearing in post-Soviet Russia. Population and Development Review, 32(4):

729-753.

Pollmann-Shult, M. (2014). Parenthood and Life Satisfaction: Why Don’t Children

Make People Happy? Journal of Marriage and Family, 76, 319–336.

Powdthavee, N. (2009). I can’t smile without you: Spousal correlation in life

satisfaction. Journal of Economic Psychology 30, 675–689.

Testa, M.R., Cavalli, L., & Rosina, A. (2011), Couples' childbearing behaviour in Italy:

which of the partners is leading it? Vienna Yearbook of Population Research,

157-178.

Van de Kaa, D. J. (1987). Europe’s Second Demographic Transition, Population

Bulletin, 42 (1), Washington, The Population Reference Bureau.

23

Tables Table 1. Descriptive statistics (%) on independent variables for each of the three sample used in the multivariate analyses.

Variable First child Second child Third+ child Man’s happiness

Less happy than usual 9.4 10.4 10.3 As usual 69.0 69.9 68.9 Happier than usual 21.7 19.7 20.8 Woman’s happiness

Less happy than usual 12.9 12.1 15.2 As usual 58.6 63.7 66.8 Happier than usual 28.4 24.2 18.0 Coabithing couple 37.4 25.2 21.4 Man's age (mean) 30.1 31.0 32.2 Woman's age (mean) 28.0 28.7 29.4 Man's health (mean) 1.9 1.9 2.1 Woman's health (mean) 2.0 2.0 2.1 Man's education level

Degree 21.2 20.1 13.2 Diploma 24.9 26.7 24.9 Vocational school 36.2 33.3 33.5 Low school level 17.7 19.9 28.4 Woman's education level

Degree 19.6 18.2 15.9 Diploma 29.4 27.8 22.5 Vocational school 39.0 41.4 39.1 Low school level 12.0 12.6 22.5 Man's working status

Inactive 2.5 2.6 4.2 Unemployed 5.7 5.9 12.1 Employed 91.8 91.5 83.6 Woman's working status

Inactive 21.9 34.5 52.8 Unemployed 3.7 2.7 2.4 Employed 74.3 62.8 44.8 N. couple-observations 2101 1706 982 N. couples 645 554 264

24

Table 2. Combination of women's and men's happiness, average values over all observations.

Men's happines Women's happines

Less happy As usual Happier Less happy 2.5 6.6 1.5 As usual 9.0 46.2 14.6 Happier 2.0 11.0 6.7

25

Table 3. Discrete-time event history logistic regression estimates for childbirth transitions as function of both partners' happiness, by parity.

Independent variables First child Second child Third+ child Man’s happiness (ref.: as usual)

Less happy than usual 0.013 -0.192 -0.137

(0.277) (0.206) (0.265)

Happier than usual 0.396** 0.006 -0.117

(0.190) (0.155) (0.208)

Woman’s happiness (ref.: as usual) Less happy than usual -0.197 -0.396** -0.014

(0.255) (0.197) (0.226)

Happier than usual 0.596*** -0.502*** 0.005

(0.175) (0.151) (0.227)

Coabithing (ref.: married) -0.866*** -0.515*** -0.429*

(0.210) (0.185) (0.257)

Man's age -0.479*** 0.171 0.270

(0.181) (0.120) (0.181)

Woman's age 0.290 -0.175 -0.195

(0.181) (0.139) (0.202)

Man's age squared 0.008*** -0.002 -0.003

(0.003) (0.002) (0.003)

Woman's age squared -0.003 0.004 0.004

(0.003) (0.002) (0.003)

Man's health 0.024 -0.028 -0.164

(0.100) (0.074) (0.107)

Woman's health -0.031 0.084 0.041

(0.094) (0.071) (0.104)

Man’s Education (ref.: degree) Diploma 0.413 0.202 0.579*

(0.273) (0.193) (0.300)

Vocational school 0.315 0.257 0.427

(0.270) (0.194) (0.301)

Low school level 0.842** 0.205 0.708**

(0.334) (0.221) (0.323)

Woman’s Education (ref.: degree) Diploma 0.348 0.274 0.485*

(0.276) (0.202) (0.287)

Vocational school 0.602** 0.076 0.282

(0.280) (0.195) (0.276)

Low school level 0.519 0.136 0.358

(0.363) (0.246) (0.320)

Employment (ref.: employed) Inactive man -0.552 0.610 0.496

(0.575) (0.377) (0.399)

Unemployed man 0.053 0.112 0.444*

(0.348) (0.249) (0.249)

Inactive woman 4.294*** 1.432*** 0.951***

26

(0.322) (0.130) (0.185)

Unemployed woman 0.761* -0.010 -0.566

(0.403) (0.561) (0.802)

t -0.026 -0.214*** -0.080

(0.070) (0.050) (0.065)

t squared 0.009** 0.015*** 0.008**

(0.004) (0.003) (0.004)

Constant -1.891 -2.711 -5.293

(2.811) (2.209) (3.221)

N. couple-observations 2101 1706 982 N. couples 645 554 264

Note: * p-value < 0.10; ** p-value < 0.05; *** p-value < 0.01.

27

Table 4. Discrete-time event history logistic regression estimates for childbirth transitions as function of couples happiness types, by parity.

Independent variables First child Second child Third+ child Couple’s happiness (ref.: man and woman at the usual level)

Man less, Woman less -0.167 -0.652*** -0.370

(0.337) (0.234) (0.250)

Man usual, Woman less 0.113 -0.378 -0.193

(0.308) (0.240) (0.276)

Man less, Woman usual 0.381 0.029 -0.313

(0.348) (0.244) (0.337)

Man more, Woman less 0.024 0.590 0.147

(0.597) (0.444) (0.512)

Man less, Woman more 0.502 -0.352 -0.618

(0.608) (0.501) (0.806)

Man more, Woman usual 0.564** -0.002 -0.139

(0.270) (0.196) (0.250)

Man usual, Woman more 0.727*** -0.401** 0.101

(0.216) (0.179) (0.277)

Man more, Woman more 0.987*** -0.711*** -0.323

(0.275) (0.269) (0.418)

Coabithing (ref.: married) -0.863*** -0.470*** -0.255

(0.207) (0.178) (0.239)

Man's age -0.487*** 0.204* 0.158

(0.178) (0.114) (0.166)

Woman's age 0.292 -0.202 -0.032

(0.180) (0.131) (0.188)

Man's age squared 0.008*** -0.003 -0.001

(0.003) (0.002) (0.002)

Woman's age squared -0.003 0.004* 0.001

(0.003) (0.002) (0.003)

Man's health 0.022 -0.039 -0.139

(0.100) (0.071) (0.098)

Woman's health -0.040 0.087 0.027

(0.094) (0.068) (0.098)

Man’s Education (ref.: degree) Diploma 0.372 0.209 0.523*

(0.279) (0.190) (0.293)

Vocational school 0.270 0.258 0.390

(0.274) (0.191) (0.294)

Low school level 0.805** 0.229 0.574*

(0.324) (0.211) (0.310)

28

Woman’s Education (ref.: degree) Diploma 0.357 0.286 0.346

(0.280) (0.197) (0.276)

Vocational school 0.557** 0.109 0.109

(0.282) (0.191) (0.264)

Low school level 0.555 0.148 0.236

(0.352) (0.236) (0.301)

Employment (ref.: employed) Inactive man -0.638 0.443 0.346

(0.544) (0.338) (0.351)

Unemployed man 0.033 0.111 0.520**

(0.346) (0.241) (0.233)

Inactive woman 4.365*** 1.396*** 0.997***

(0.316) (0.126) (0.177)

Unemployed woman 0.872** 0.132 -0.456

(0.400) (0.510) (0.795)

t -0.005 -0.226*** -0.067

(0.070) (0.048) (0.062)

t squared 0.008** 0.016*** 0.007*

(0.004) (0.003) (0.004)

Constant -1.848 -2.775 -5.820*

(2.765) (2.135) (3.096)

N. couple-observations 2101 1706 982 N. couples 645 554 264

Note: * p-value < 0.10; ** p-value < 0.05; *** p-value < 0.01.